


"""simple docstring"""






from dataclasses import dataclass, field
from typing import Optional




@dataclass
class 							A_      :
  lowerCAmelCase__      			=	field(
      default='codeparrot/codeparrot'     ,	metadata={'help': 'Model name or path of model to be trained.'} )
  lowerCAmelCase__      			=	field(
      default='./'     ,	metadata={'help': 'Save dir where model repo is cloned and models updates are saved to.'} )
  lowerCAmelCase__      			=	field(
      default='codeparrot/codeparrot-clean-train'     ,	metadata={'help': 'Name or path of training dataset.'} )
  lowerCAmelCase__      			=	field(
      default='codeparrot/codeparrot-clean-valid'     ,	metadata={'help': 'Name or path of validation dataset.'} )
  lowerCAmelCase__      			=	field(default=2     ,	metadata={'help': 'Batch size for training.'} )
  lowerCAmelCase__      			=	field(default=2     ,	metadata={'help': 'Batch size for evaluation.'} )
  lowerCAmelCase__      			=	field(default=0.1     ,	metadata={'help': 'Value of weight decay.'} )
  lowerCAmelCase__      			=	field(
      default=1_0_0_0_0     ,	metadata={'help': 'Size of buffer used to shuffle streaming dataset.'} )
  lowerCAmelCase__      			=	field(default=2E-4     ,	metadata={'help': 'Learning rate fo training.'} )
  lowerCAmelCase__      			=	field(default='cosine'     ,	metadata={'help': 'Learning rate.'} )
  lowerCAmelCase__      			=	field(
      default=7_5_0     ,	metadata={'help': 'Number of warmup steps in the learning rate schedule.'} )
  lowerCAmelCase__      			=	field(
      default=1_6     ,	metadata={'help': 'Number of gradient accumulation steps.'} )
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'} )
  lowerCAmelCase__      			=	field(default=5_0_0_0_0     ,	metadata={'help': 'Maximum number of training steps.'} )
  lowerCAmelCase__      			=	field(
      default=-1     ,	metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'} )
  lowerCAmelCase__      			=	field(default=1_0_2_4     ,	metadata={'help': 'Sequence lengths used for training.'} )
  lowerCAmelCase__      			=	field(default=1     ,	metadata={'help': 'Training seed.'} )
  lowerCAmelCase__      			=	field(
      default=1_0_2_4     ,	metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'}     ,	)
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={'help': 'States path if the training should continue from a checkpoint folder.'} )
  lowerCAmelCase__      			=	field(default=_a     ,	metadata={'help': 'If True the data is pretokenized.'} )




@dataclass
class 							A_      :
  lowerCAmelCase__      			=	field(
      default='codeparrot/codeparrot'     ,	metadata={'help': 'Model name or path of model to be evaluated.'} )
  lowerCAmelCase__      			=	field(
      default='codeparrot/codeparrot-clean-valid'     ,	metadata={'help': 'Name or path of validation dataset.'} )
  lowerCAmelCase__      			=	field(default=2     ,	metadata={'help': 'Batch size used for evaluation.'} )
  lowerCAmelCase__      			=	field(
      default=-1     ,	metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'} )
  lowerCAmelCase__      			=	field(default=1_0_2_4     ,	metadata={'help': 'Length of sequences to be evaluated.'} )
  lowerCAmelCase__      			=	field(default=1     ,	metadata={'help': 'Random seed used for evaluation.'} )




@dataclass
class 							A_      :
  lowerCAmelCase__      			=	field(
      default='codeparrot/codeparrot'     ,	metadata={'help': 'Model name or path of model to be evaluated.'} )
  lowerCAmelCase__      			=	field(default=_a     ,	metadata={'help': 'Number of workers used for code evaluation.'} )
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}     ,	)
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={'help': 'Sample from the language model\'s output distribution.'} )
  lowerCAmelCase__      			=	field(default=0.2     ,	metadata={'help': 'Sampling temperature used for generation.'} )
  lowerCAmelCase__      			=	field(default=2_5_6     ,	metadata={'help': 'Maximum number of newly generated tokens.'} )
  lowerCAmelCase__      			=	field(default=0     ,	metadata={'help': 'Top-k parameter used for generation.'} )
  lowerCAmelCase__      			=	field(default=0.95     ,	metadata={'help': 'Top-p parameter used for nucleus sampling.'} )
  lowerCAmelCase__      			=	field(default=1_0     ,	metadata={'help': 'Number of generations to run in parallel.'} )
  lowerCAmelCase__      			=	field(
      default=2_0_0     ,	metadata={'help': 'Number of completions to generate for each sample.'} )
  lowerCAmelCase__      			=	field(default=1     ,	metadata={'help': 'Random seed used for evaluation.'} )
  lowerCAmelCase__      			=	field(
      default='eval_results.json'     ,	metadata={'help': 'Random seed used for evaluation.'} )
  lowerCAmelCase__      			=	field(
      default='0'     ,	metadata={'help': 'Allow `code_eval` to execute Python code on machine'} )
  lowerCAmelCase__      			=	field(
      default=-1     ,	metadata={
          'help': (
              'Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive'
              ' number corresponds to which GPU device id to run on.'
          )
      }     ,	)




@dataclass
class 							A_      :
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={
          'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
      }     ,	)
  lowerCAmelCase__      			=	field(
      default='transformersbook/codeparrot'     ,	metadata={'help': 'Folder or name of dataset to process.'} )
  lowerCAmelCase__      			=	field(
      default='codeparrot-clean'     ,	metadata={'help': 'Folder to save processed processed dataset.'} )
  lowerCAmelCase__      			=	field(
      default=1_0_0_0_0_0     ,	metadata={'help': 'Number of files to save per JSON output file.'} )
  lowerCAmelCase__      			=	field(default='content'     ,	metadata={'help': 'Column containing text data to process.'} )
  lowerCAmelCase__      			=	field(
      default=1_0_0_0     ,	metadata={'help': 'Maximum line length in file, otherwise file is filtered.'} )
  lowerCAmelCase__      			=	field(
      default=1_0_0     ,	metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'} )
  lowerCAmelCase__      			=	field(
      default=0.25     ,	metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'} )
  lowerCAmelCase__      			=	field(
      default=1.5     ,	metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'} )
  lowerCAmelCase__      			=	field(
      default=0.7     ,	metadata={'help': 'Probability for filtering config, test and uncommon files.'} )
  lowerCAmelCase__      			=	field(
      default='codeparrot/codeparrot'     ,	metadata={'help': 'Name or path to the tokenizer.'}     ,	)
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={'help': 'If True, near-duplicate samples are removed.'} )
  lowerCAmelCase__      			=	field(
      default=0.85     ,	metadata={'help': 'Jaccard threshold for near-duplicate samples.'} )




@dataclass
class 							A_      :
  lowerCAmelCase__      			=	field(
      default='gpt2'     ,	metadata={'help': 'Base tokenizer to build new tokenizer from.'} )
  lowerCAmelCase__      			=	field(
      default='transformersbook/codeparrot-train'     ,	metadata={'help': 'Dataset to train tokenizer on.'} )
  lowerCAmelCase__      			=	field(default='content'     ,	metadata={'help': 'Column containing text data to process.'} )
  lowerCAmelCase__      			=	field(default=2_0_0_0_0_0     ,	metadata={'help': 'Number of examples to train tokenizer on.'} )
  lowerCAmelCase__      			=	field(
      default=3_2_7_6_8     ,	metadata={'help': 'Number of examples to train the tokenizer on.'} )
  lowerCAmelCase__      			=	field(default='codeparrot'     ,	metadata={'help': 'Name of new tokenizer.'} )
  lowerCAmelCase__      			=	field(default=_a     ,	metadata={'help': 'Push saved tokenizer to the hub.'} )




@dataclass
class 							A_      :
  lowerCAmelCase__      			=	field(
      default='codeparrot/codeparrot'     ,	metadata={'help': 'Name or path to the tokenizer.'} )
  lowerCAmelCase__      			=	field(
      default='codeparrot/codeparrot-clean-train'     ,	metadata={'help': 'Name or path to the dataset to pretokenize.'} )
  lowerCAmelCase__      			=	field(
      default='tokenized-codeparrot-train'     ,	metadata={'help': 'Repo name of the pretokenized data.'} )
  lowerCAmelCase__      			=	field(default=_a     ,	metadata={'help': 'Number of workers used for code evaluation.'} )




@dataclass
class 							A_      :
  lowerCAmelCase__      			=	field(
      default='gpt2-large'     ,	metadata={'help': 'Configuration to use for model initialization.'} )
  lowerCAmelCase__      			=	field(
      default='codeparrot/codeparrot'     ,	metadata={'help': 'Tokenizer attached to model.'} )
  lowerCAmelCase__      			=	field(default='codeparrot'     ,	metadata={'help': 'Name of the created model.'} )
  lowerCAmelCase__      			=	field(default=_a     ,	metadata={'help': 'Push saved tokenizer to the hub.'} )


import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar







class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					Dict      		=					logging.get_logger()

														# the current default level is logging.WARNING
														A      :					List[Any]      		=					logging.get_verbosity()

														logging.set_verbosity_error()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														logging.set_verbosity_warning()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														logging.set_verbosity_info()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														logging.set_verbosity_debug()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														# restore to the original level
														logging.set_verbosity(__UpperCAmelCase						)




							def       snake_case			(					self						)		->       str:
														A      :					Any      		=					logging.get_verbosity()

														A      :					Optional[Any]      		=					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)
														A      :					Tuple      		=					'''Testing 1, 2, 3'''

														# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
														if level_origin <= logging.WARNING:
																					with CaptureLogger(__UpperCAmelCase						) as cl:
																												logger.warning(__UpperCAmelCase						)
																					self.assertEqual(cl.out    ,						msg + '''\n'''						)

														# this is setting the level for all of `transformers.*` loggers
														logging.set_verbosity_error()

														# should not be able to log warnings
														with CaptureLogger(__UpperCAmelCase						) as cl:
																					logger.warning(__UpperCAmelCase						)
														self.assertEqual(cl.out    ,						''''''						)

														# should be able to log warnings again
														logging.set_verbosity_warning()
														with CaptureLogger(__UpperCAmelCase						) as cl:
																					logger.warning(__UpperCAmelCase						)
														self.assertEqual(cl.out    ,						msg + '''\n'''						)

														# restore to the original level
														logging.set_verbosity(__UpperCAmelCase						)




							@mockenv(TRANSFORMERS_VERBOSITY='''error'''						)
							def       snake_case			(					self						)		->       Optional[int]:
														# reset for the env var to take effect, next time some logger call is made
														transformers.utils.logging._reset_library_root_logger()
														# this action activates the env var
														A      :					int      		=					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)

														A      :					Any      		=					os.getenv('''TRANSFORMERS_VERBOSITY'''    ,						__UpperCAmelCase						)
														A      :					List[str]      		=					logging.log_levels[env_level_str]

														A      :					Optional[int]      		=					logging.get_verbosity()
														self.assertEqual(
														    __UpperCAmelCase    ,						__UpperCAmelCase    ,						f'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'    ,						)

														# restore to the original level
														A      :					str      		=					''''''
														transformers.utils.logging._reset_library_root_logger()




							@mockenv(TRANSFORMERS_VERBOSITY='''super-error'''						)
							def       snake_case			(					self						)		->       Optional[int]:
														# reset for the env var to take effect, next time some logger call is made
														transformers.utils.logging._reset_library_root_logger()
														A      :					str      		=					logging.logging.getLogger()
														with CaptureLogger(__UpperCAmelCase						) as cl:
																					# this action activates the env var
																					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)
														self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''    ,						cl.out						)

														# no need to restore as nothing was changed






							def       snake_case			(					self						)		->       Optional[int]:
														# testing `logger.warning_advice()`
														transformers.utils.logging._reset_library_root_logger()

														A      :					Union[str, Any]      		=					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)
														A      :					Optional[int]      		=					'''Testing 1, 2, 3'''

														with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''						):
																					# nothing should be logged as env var disables this method
																					with CaptureLogger(__UpperCAmelCase						) as cl:
																												logger.warning_advice(__UpperCAmelCase						)
																					self.assertEqual(cl.out    ,						''''''						)

														with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''						):
																					# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
																					with CaptureLogger(__UpperCAmelCase						) as cl:
																												logger.warning_advice(__UpperCAmelCase						)
																					self.assertEqual(cl.out    ,						msg + '''\n'''						)






def        snake_case__		(    ):
							disable_progress_bar()
							assert are_progress_bars_disabled()

							enable_progress_bar()
							assert not are_progress_bars_disabled()






